uncleflo
Registered since September 28th, 2017
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Setting Up Your Own Airstrip - FlyingLessons.co.uk - UK
Saved by uncleflo on January 31st, 2019.
In addition to the UK’s civil and military aerodromes, there are hundreds of smaller, grass airfields set up by private individuals (termed ‘farm strips’). If you own suitable land, it’s possible to set up your own – of course, planning restrictions and practical considerations apply. We outline some of them here. Naturally, we suggest you obtain professional advice before flying from your land – however, if you do own a suitable portion of real estate, you can operate your aircraft from it for up to 28 days per year without planning permission, providing it’s safe to do. If the usage exceeds that, then you’ll need to apply to your Local Authority for change of use permission. Airfields operating under the 28 day rule are classified as ‘unlicensed fields’. This isn’t to say they’re illegal, but rather that they haven’t been subject to a Civil Aviation Authority survey; which quantifies and assesses approach obstacles, runway surfaces & length, and so forth. Your farm strips is therefore likely to be more challenging to fly in and out of – but, as most farm strip flyers will tell you, therein lies the challenge!
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How TF-IDF algorithm determines keyword importance - arbitrue Blog
Saved by uncleflo on December 23rd, 2018.
There are many tools in the developer’s toolbox when it comes to automatic data extraction. A good example is TF-IDF algorithm (Term Frequency – Inverse Document Frequency) which helps the system understand the importance of keywords extracted using OCR. Here’s how TF-IDF can be used for invoice and receipt recognition. In this article we focus on other techniques in order to make this text file “understandable” to a computer. For this purpose, we must delve into the world of NLP or Natural Language Processing. We will focus mainly on how we can transform our file of raw text into a format that will easily be understandable by our algorithm. In a nutshell, TF-IDF is a technique for understanding how important a word is in a document which is often used as a weighting factor for numerous use cases. TF-IDF takes under consideration how frequent a word appears in a single document in relation to how frequent that word is in general. Search engines can use TF-IDF to determine which results are the most relevant for a search query.
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Weighting words using Tf-Idf - NLP-FOR-HACKERS
Saved by uncleflo on December 23rd, 2018.
If I ask you “Do you remember the article about electrons in NY Times?” there’s a better chance you will remember it than if I asked you “Do you remember the article about electrons in the Physics books?”. Here’s why: an article about electrons in NY Times is far less common than in a collection of physics books. It is less likely to stumble upon the “electron” concept in NY Times than in a physics book. Let’s consider now the scenario of a single article. Suppose you read an article and you’re asked to rank the concepts found in the article by importance. The chances are you’ll basically order the concepts by frequency. The reason is simply that important stuff would be mentioned repeatedly because the narrative gravitates around them. Combining the 2 insights, given a term, a document and a collection of documents we can loosely say that:importance ~ appearances(term, document) / count(documents containing term in collection).
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Torsion of Open Thin Wall Sections
Saved by uncleflo on September 2nd, 2014.
The treatment of torsion in most Mechanics of Materials textbooks is uneven. While circular cross sections are analyzed in detail (sometimes too thoroughly), the coverage of non-circular sections is typically relegated to “advanced material” covering 4-5 pages. (In this respect Beer-Johnston- DeWolf is somewhat better than Vable, who totally omits Open Thin Wall sections)
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